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Visual Analysis of Humans pp 225–251Cite as

Feature-Based Pose Estimation

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Abstract

In this chapter we review challenges and methodology for feature-based predictive three-dimensional human pose reconstruction, based on image and video data. We argue that reliable 3D human pose prediction can be achieved through an alliance between image descriptors that encode multiple levels of selectivity and invariance and models that are capable to represent multiple structured solutions. For monocular systems, key to reliability is the capacity to leverage prior knowledge in order to bias solutions not only to kinematically feasible sets, but also toward typical configurations that humans are likely to assume in everyday surroundings. In this context, we discuss several predictive methods including large-scale mixture of experts, supervised spectral latent variable models and structural support vector machines, asses the impact of the various choices of image descriptors, review open problems, and give pointers to datasets and code available online.

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Notes

  1. 1.

    Standard kernel regression models can be viewed as a special case of a conditional mixture with a single expert (M=1). We will thus not include separate derivations for kernel regression, the restriction to M=1 in (12.1) being straightforward.

  2. 2.

    Sampled configurations have parenthesized superscripts; subscripts index training datapoints.

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Acknowledgements

This work has been supported in part by the European Commission under MCEXT-025481 and by CNCSIS-UEFISCU under project PN II-RU-RC-2/2009. This chapter reviews research presented in [9, 11, 25, 28].

Author information

Authors and Affiliations

  1. Institute for Numerical Simulation (INS), Faculty of Mathematics and Natural Science, University of Bonn, Bonn, Germany

    Cristian Sminchisescu

  2. Institute for Mathematics of the Romanian Academy (IMAR), Bucharest, Romania

    Cristian Sminchisescu

  3. University of Washington, Seattle, USA

    Liefeng Bo

  4. INS, University of Bonn, Bonn, Germany

    Catalin Ionescu

  5. ObjectVideo, Reston, VA, USA

    Atul Kanaujia

Authors
  1. Cristian Sminchisescu
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  2. Liefeng Bo
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  3. Catalin Ionescu
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  4. Atul Kanaujia
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Correspondence to Cristian Sminchisescu .

Editor information

Editors and Affiliations

  1. Department of Media Technology, Aalborg University, Niels Jernes Vej 14, Aalborg, 9220, Denmark

    Thomas B. Moeslund

  2. Centre for Vision, Speech & Signal Proc., University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom

    Adrian Hilton

  3. Copenhagen Institute of Technology, Aalborg University, Lautrupvang 2B, Ballerup, 2750, Denmark

    Volker Krüger

  4. Disney Research, Forbes Avenue 615, Pittsburgh, 15213, Pennsylvania, USA

    Leonid Sigal

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Sminchisescu, C., Bo, L., Ionescu, C., Kanaujia, A. (2011). Feature-Based Pose Estimation. In: Moeslund, T., Hilton, A., Krüger, V., Sigal, L. (eds) Visual Analysis of Humans. Springer, London. https://doi.org/10.1007/978-0-85729-997-0_12

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  • DOI: https://doi.org/10.1007/978-0-85729-997-0_12

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-996-3

  • Online ISBN: 978-0-85729-997-0

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